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However, these methods focus mainly on individual features and rarely consider the lockstep behaviours among attack users, which suffer from low precision in detecting group shilling attacks. In this work, we propose a three-stage detection method based on strong lockstep behaviours among group members and group behaviour features for detecting group shilling attacks. First, we construct a weighted user relationship graph by combining direct and indirect collusive degrees between users. Second, we find all dense subgraphs in the user relationship graph to generate a set of suspicious groups by introducing a topological potential method. Finally, we use a clustering method to detect shilling groups by extracting group behaviour features. Extensive experiments on the Netflix and sampled Amazon review datasets show that the proposed approach is effective for detecting group shilling attacks in recommender systems, and the F1-measure on two datasets can reach over 99 percent and 76 percent, respectively.<\/jats:p>","DOI":"10.1155\/2021\/2907691","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T01:35:57Z","timestamp":1632792957000},"page":"1-18","source":"Crossref","is-referenced-by-count":9,"title":["An Unsupervised Approach for Detecting Group Shilling Attacks in Recommender Systems Based on Topological Potential and Group Behaviour Features"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1175-429X","authenticated-orcid":true,"given":"Hongyun","family":"Cai","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"},{"name":"School of Cyber Security and Computer, Hebei University, Baoding, China"},{"name":"The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, China"},{"name":"The Key Laboratory for Software Engineering of Hebei Province, Qinhuangdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9595-3589","authenticated-orcid":true,"given":"Fuzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Yanshan University, Qinhuangdao, China"},{"name":"The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Qinhuangdao, China"},{"name":"The Key Laboratory for Software Engineering of Hebei Province, Qinhuangdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-7637-6","volume-title":"Recommender Systems Handbook","author":"F. 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